Abstract

Abstract
The number of everyday interconnected devices continues to increase and constitute the Internet of Things (IoT). Things are small computers equipped with sensors and wireless communications capabilities that are driven by energy constraints, since they use batteries and may be required to operate over long periods of time. The majority of these devices perform data collection. The collected data is stored on-line using web-services that, sometimes, operate without any special considerations regarding security and privacy. The current work proposes a modified hash-chain authentication mechanism that, with the help of a smartphone, can authenticate each interaction of the devices with a REST web-service using One Time Passwords (OTP) while using open wireless networks. Moreover, the proposed authentication mechanism adheres to the stateless, HTTP-like behavior expected of REST web-services, even allowing the caching of server authentication replies within a predefined time window. No other known web-service authentication mechanism operates in such manner

Abstract
The number of everyday interconnected devices continues to increase and constitute the Internet of Things (IoT). Things are small computers equipped with sensors and wireless communications capabilities that are driven by energy constraints, since they use batteries and may be required to operate over long periods of time. The majority of these devices perform data collection. The collected data is stored on-line using web-services that, sometimes, operate without any special considerations regarding security and privacy. The current work proposes a modified hash-chain authentication mechanism that, with the help of a smart-phone, can authenticate each interaction of the devices with a REST web-service using One Time Passwords (OTP). Moreover, the proposed authentication mechanism adheres to the stateless, HTTP-like behavior expected of REST web-services, even allowing the caching of server authentication replies within a predefined time window. No other known web-service authentication mechanism operates in such manner.

Abstract
Smart Cities are a key application domain for the Internet of Things (IoT), and it is coming nearer everyday through pilot trials and deployments in various cities around the world. In Porto, Portugal, a city-wide IoT Living Lab emerged after we deployed several testbeds, e.g. harbour and a city-scale vehicular networks, and carried out various experiments with the SenseMyCity crowdsensor. In this paper, we discuss how a standard Machine-to-Machine (M2M) middleware is a key enabler of our e-health platform and SenseMyCity crowdsensor, powered by the use of smartphones as M2M gateways. M2M standards provided by ETSI/oneM2M are essential for a paradigm shift, aiming at making the IoT truly interoperable without the need for human intervention. In this work, we map two applications that rely on the role of a smartphone as a gateway, which acts as a proxy to connect legacy devices to the IoT using a standard middleware. We illustrate the advantages of using M2M, and, as a proof-of-concept, we measure and quantify the energy savings obtained, showing improvements of smartphones' battery life.

Abstract
Due to the differences in terms of antenna height, scatterer density, and relative speed, V2I links exhibit different propagation characteristics compared to V2V links. We develop a geometry-based path loss and shadow fading model for V2I links. We separately model the following types of V2I links: line-of-sight, non-line-of-sight due to vehicles, non-line-of-sight due to foliage, and non-line-of-sight due to buildings. We validate the proposed model using V2I field measurements. We implement the model in the GEMV2 simulator, and make the source code publicly available.

Abstract
Cloud computing is the latest trend in business for providing software, platforms and services over the Internet. However, a widespread adoption of this paradigm has been hampered by the lack of security mechanisms. In view of this, the aim of this work is to propose a new approach for detecting anomalies in cloud network traffic. The anomaly detection mechanism works on the basis of a Support Vector Machine (SVM). The key requirement for improving the accuracy of the SVM model, in the context of cloud, is to reduce the total amount of data. In light of this, we put forward the Poisson Moving Average predictor which is the core of the feature extraction approach and is able to handle the vast amount of information generated over time. In addition, two case studies are employed to validate the effectiveness of the mechanism on the basis of real datasets. Compared with other approaches, our solution exhibits the best performance in terms of detection and false alarm rates.